Identifying Gene Signature in RNA Sequencing Multiple Sclerosis Data

Objectives: Multiple Sclerosis (MS) is a complex central nervous system disease; it is the result of a combination of genetic predispositions and a nongenetic trigger. This study aims to find the gene signatures using a Pareto optimization algorithm for MS RNA sequencing (RNA-seq) data. Methods: This case-control study involved 50 samples (25 MS patients and 25 age-matched healthy individuals) and their GSE profiles (GSE123496) were selected from the National Center for Biotechnology Information Gene Expression Omnibus database. We used Pareto-optimal cluster size identification to find the gene signatures in the RNA-seq data. After prefiltering and normalizing the data, we used the Limma package to find the differentially expressed genes (DEGs). The Pareto-optimal cluster size for these DEGs was then determined using the technique, multi-objective optimization for collecting the clusters alternatives. Afterward, the RNA-seq data were clustered via k-means with suitable cluster size. The best cluster, as a signature, was found by calculating the mean of the Spearman correlation coefficients (SCCs) of whole genes in the module in a pairwise manner. All analysis was performed in the R software, 4.1.1 package, under virtual space with 100 GB RAM. Results: In total, 960 DEGs were identified by the Limma analysis. Among them, 720 were up-regulated genes and 240 were down-regulated genes. Meanwhile, 6 Pareto-optimal clusters were obtained. Two clusters that had the greatest average SCCs score (0.88 and 0.74, respectively) were chosen as the gene signatures. Discussion: A total of 9 metabolic prognostic genes and 3 biological pathways were identified. These can provide more potent prognostic information for MS patients

Determine the most powerful predictor of the body image and its association with gender and body mass index in adolescent at school age in Ahvaz, South of Iran

Introduction: The body image focuses on how to understand, think, feel, body and appearance of the body. At the age of school and adolescence, girls and boys experience different situations, so this can affect their mental image. Therefore, the aim of this study was to determine the most powerful predictor of the body image and its association with gender and body mass index in adolescent at school age in Ahvaz. Methods: This is a descriptive-analytic epidemiologic study that examines body image in 458 high school girls and boys and its correlation with body mass index in Ahvaz city in 2018. Sampling was done by random cluster method. The data gathering tool was a demographic questionnaire , anthropometric information check list and self-body multi-dimensional relationship questionnaire to examine the mental image of the body. Data were collected using SPSS software version 17 and descriptive statistical tests and analytical at a significant level of 95%. Results: The results of this study showed that the mean weight and grade point average were significantly higher in female sex. Three dimensions of mental image had a significant correlation with BMI, but overall, the mental image did not have a significant correlation with BMI. It was also the strongest predictor of the student's image, so that there was an inverse and significant relationship between the level of education and the score of mental image. Conclusion: The results indicated. there was no difference in body satisfaction in female and male subjects and BMI, and also there was a significant and inverse relationship between education level and mental image score. &nbsp